A female employee at JSW Vijayanagar’s steel manufacturing unit had taken on the additional role and responsibility of payment activities in fiscal year 2019. She successfully handled the corporate steel business payments without any escalation or error.

The company noticed that her response and turnaround time on all queries from business were prompt and accurate. Additionally, she would take the initiative of maximising efficiency and improving customer service by fixing errors or looking at ways to streamline procedures. Through these efforts, she displayed strong analytical skills.

This employee soon became a strong backup for other businesses as well. Since she wouldn’t take any unplanned leaves and adjusted them whenever an unexpected situation arose, she also displayed a behaviour of a disciplined worker. Data patterns, however, revealed that she was merely given ‘fair’ ratings in the previous years. JSW, which now relies on data analytics to ensure a fair process during appraisals, promptly calibrated her rating upwards.

Through data analytics, JSW has been able to control double-rating jumps or drops, which may have been unilaterally given by team managers. Data patterns helped the organisation in identifying managers who had given double jumps in ratings to certain employees. Through discussions, such managers and their teams were made part of a systematic hand-holding during the appraisal process for an objective performance review. JSW Group president (HR) Gautam Chainani said, “Identification of such abnormalities and subsequent course-correction measures enabled us to control double jumps by 50%.”

Given that objectivity is more important during appraisals than human emotions, many organisations have adopted data analytics to ensure the process of appraisal is fair and transparent. In addition to weeding out anomalies during appraisals, data analytics ensures performance differentiation gets sharper every year.

At Mahindra & Mahindra, too, interventions are based on analytics. Rating discrepancies following transfers or role changes, leading to prevention of personal biases or likes/dislikes of managers, for instance, are calibrated by the organisation using analytics. Variations of ratings in case of maternity breaks after a lady employee joins back are now easily spotted through analytics to eliminate discrimination, if any. Year-on-year flip-flop in ratings and lack of consistency in assessment have also been probed through analytics to ensure fairness of the process.

Mahindra & Mahindra chief people officer Rajeshwar Tripathi said, “With an officer strength of over 14,000, data analytics plays an important role in ensuring checks and balances in the appraisal process. It helps in identifying anomalies or biases if any, by throwing up a comparative analysis of an employee’s performance, by combining historic and real-time data. This enables us to raise questions and take timely actions.”

At RPG Enterprises, which does not use analytical tools, while every employee is rated by their supervisor against their goals, these ratings are discussed in depth and re-looked at by their skip-level supervisors, and later by the management team members.

RPG Enterprises chief talent officer Supratik Bhattacharyya said, “We analyse employees’ past scores, and if we see significant rise or dips, we ensure that these are corroborated with relevant and logical reasoning. There are enough examples of employees who may not have achieved what they had aimed for, yet we have rated them high because of their genuine efforts. There are multiple instances when we have rated failures with high scores, simply because they tried new things and failed. At senior levels, we always take an average of three-year ratings to arrive at their annual scores.”